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First MOOCs, now MOOLs: Massive Online Open Laboratories

Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models—even at the secondary structure level—hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies—including several previously unrecognized negative design rules—were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

Click here to read more from this December 2013 PNAS article by Jeehyung Lee et al.

The WeFold “coopetition” model

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Click here to read more from this February 2014 Proteins article by George Khoury et al.

Using contents to help crunch big data

Advances in biotechnology have fueled the generation of unprecedented quantities of data across the life sciences. However, finding analysts who can address such ‘big data’ problems effectively has become a significant research bottleneck. Historically, prize-based contests have had striking success in attracting unconventional individuals who can overcome difficult challenges. To determine whether this approach could solve a real big-data biologic algorithm problem, we used a complex immunogenomics problem as the basis for a two-week online contest broadcast to participants outside academia and biomedical disciplines. Participants in our contest produced over 600 submissions containing 89 novel computational approaches to the problem. Thirty submissions exceeded the benchmark performance of the US National Institutes of Health’s MegaBLAST. The best achieved both greater accuracy and speed (1,000 times greater). Here we show the potential of using online prize-based contests to access individuals without domain-specific backgrounds to address big-data challenges in the life sciences.

Click here to read more from this February 2013 Nature article by Karim Lakhani et al.

Experimenting in open innovation at Harvard Medical School

Harvard Medical School, with some of the best researchers and practitioners of medicine in the world, would seem an unlikely candidate to embrace open innovation. There is certainly no shortage of talent and motivation at Harvard to drive for breakthroughs in medicine. Yet Harvard Catalyst’s experience in opening the innovation system shows that there is significant scope and advantage in adopting open-innovation approaches among even the most elite R&D organizations. The lessons from these experiments, however, are not limited in application to academic medical centers. All organizations that have a mandate to innovate, whether creating the next great cereal product for the consumer market or solving an extremely difficult big-data analytics problem, can benefit from applying a dose of open-innovation principles to their existing innovation processes.

Click here to read more from this Spring 2013 article in the MIT Sloan Management Review by Eva Guinan, Kevin J. Boudreau and Karim R. Lakhani

Problem solving using broadcast search

An emerging perspective on the knowledge-based theory of the firm has argued that problem-solving effectiveness is key to superior organizational performance (Nickerson and Zenger 2004, Nonaka 1994, Nonaka and von Krogh 2009). Managers inside firms have to both select high-value problems to be solved, and, depending on the decomposability of those problems, choose to have them solved through internal hierarchies or external markets (Nickerson and Zenger 2004). Although a bulk of the problems firms face may be solved through a combination of internal experienced-based local (March 1991), cognitive (Gavetti and Levinthal 2000), and analogical (Gavetti et al. 2005) search processes, there may still remain some problems that, for a variety of reasons (lack of appropriate knowledge, lack of capacity, need for novel ideas), cannot be solved internally and need to be sent outside.

Click here to read more from this February 2010 article in Organization Science by Lars Bo Jeppesen and Karim R. Lakhani.